Brain-Controlled Robot Enables the Paraplegic Implement Autonomous Multimode Walk Training
Bibliographic record
Abstract
Implementation of the autonomous walk training plays an important role for patients with lower limb paralysis, which however is still an open question presently due to the extreme difficulty of accurately recognizing the patients’ motor intentions in a natural way. In this study, a brain-controlled robot system, mainly consisting of a noninvasive brain–computer interface (BCI) and an elaborately designed lower limb rehabilitation robot, was developed to enable the paralyzed patients to implement the autonomous multimode walk training. First, an enhanced motor imagery based BCI paradigm was designed to improve the subjects’ imagination abilities to generate more separable electroencephalogram (EEG) data. Then, a concept of reaction time was introduced to select the valid EEG samples, and a rhythm combination, consisting of the most complete related sensorimotor rhythms to date, was designed to fully consider their influence. The reaction time, the rhythm combination, and the key parameters of the EEG decoder were collaboratively optimized to realize accurate and robust recognition of the subjects’ motor intentions. Moreover, a human–computer mutual learning based coevolution strategy was proposed, by which the subject and the decoder can be regulated to suit each other to obtain the satisfactory online performance. Finally, the proposed methods were deployed on the brain-controlled robot system, by which multimode walk training can be implemented autonomously. 18 subjects including 9 paraplegic patients were recruited in the experiments, and all of them successfully implemented the autonomous walk training after only about 25 minutes in total for EEG data recording and model training.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".